The invention relates generally to comparative image analysis and in particular to a method for automating comparison of images for analysis and diagnosis using computer assisted detection and/or diagnosis (CAD) algorithms.
Various technical fields engage in some form of image evaluation and analysis for monitoring, analysis, or diagnostic purposes. For example, medical imaging technologies produce various types of diagnostic images which a doctor or radiologist may review for the presence of identifiable features of diagnostic significance, such as lesions, calcifications, nodules, and so forth. Similarly, in other fields, other features may be of interest. For example, industrial quality control applications may review non-invasively acquired images for the presence of internal or external cracks, fractures, or fissures. Similarly, non-destructive imaging of package and baggage contents, analysis of satellite image data and others may be reviewed to identify and classify recognizable features.
For example, in conventional mammography a radiologist examines two-dimensional (2D) X-ray images of the breast for signs of disease. It is common practice for the radiologist to compare the latest 2D X-ray images with a patient's previous 2D X-ray images, possibly going back several years over several exams, to look for signs of change that may indicate disease. Such a comparison of images acquired of the same region but at different times is known as a longitudinal comparison. It is also common practice to compare images of symmetrically related regions acquired at the same time, such as images of the right and left breasts acquired during the same mammography examination, to look for asymmetries that may indicate disease. Such a comparison of images acquired at the same time of symmetrically related regions is known as a lateral comparison.
Such longitudinal and lateral comparisons, however, may be more complex, and therefore more difficult, where a comparison of three-dimensional (3D) tomographic images is desired. Furthermore, as computing power and imaging technology advance, such 3D imaging technologies and images become more prevalent. For example, in the context of medical imaging, limited angle tomography, e.g., tomosynthesis, X-ray spin, computed tomography (CT), ultrasound, positron emission tomography (PET), single positron emission computed tomography (SPECT), and magnetic resonance imaging (MRI) are all example of 3D imaging technologies that are used for screening and diagnostic purposes with increasing frequency. As a result, the difficulties in manually performing longitudinal and/or lateral comparisons are also increasingly common. Additionally, in some cases where a longitudinal comparison is desired the radiologist may be required to compare a current 3D tomographic image to a previously acquired 2D X-ray image. Comparison of such different types of images, i.e., 2D and 3D images, acquired using different imaging modalities may be difficult, imprecise, and time-consuming for a radiologist to perform manually.
It is therefore desirable to provide an efficient and improved detection or diagnosis method and system for automating the comparative analysis and/or change detection.